Forecasting Austrian IPOs: An application of linear and neural network error-correction models

Arbeitspapier

In this paper we apply cointegration and Granger-causality analyses to construct linear and neural network error-correction models for an Austrian Initial Public Offerings IndeX (IPOXATX). We use the significant relationship between the IPOXATX and the Austrian Stock Market Index ATX to forecast the IPOXATX. For prediction purposes we apply augmented feedforward neural networks whose architecture is determined by Sequential Network Construction with the Schwartz Information Criterion as an estimator for the prediction risk. Trading based on the forecasts yields results superior to Buy and Hold or Moving Average trading strategies in terms of mean-variance considerations.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Reihe Ökonomie / Economics Series ; No. 18

Classification and Topics

• Classification: Wirtschaft ; Forecasting Models; Simulation Methods ; Neural Networks and Related Topics ; Index Numbers and Aggregation; Leading indicators ; Asset Pricing; Trading Volume; Bond Interest Rates
• Subject: initial public offerings ; neural networks ; stock market index ; cointegration analysis

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Haefke, Christian ; Helmenstein, Christian
• Event: Veröffentlichung
• (who): Institute for Advanced Studies (IHS)
• (where): Vienna
• (when): 1995

Further information

• Handle: http://hdl.handle.net/10419/68643
• Last update: 10.03.2025, 11:44 AM CET